The present disclosure provides systems and methods for on-device machine learning. In particular, the present disclosure is directed to an on-device machine learning platform and associated techniques that enable on-device prediction, training, example collection, and/or other machine learning tasks or functionality. The on-device machine learning platform can include a context provider that securely injects context features into collected training examples and/or client-provided input data used to generate predictions/inferences. Thus, the on-device machine learning platform can enable centralized training example collection, model training, and usage of machine-learned models as a service to applications or other clients.
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3. The computing device of claim 2, wherein storing the new training example in the centralized example database comprises storing the new training example in the centralized example database according to one or more options parameters that have been previously defined for the first application via the collection application programming interface, wherein the one or more options parameters comprise at least a time-to-live parameter that defines a period of time for which training examples are stored.
10. The computing device of claim 1, wherein the computing device comprises a mobile computing device and the plurality of applications comprise one or more mobile applications.
13. The one or more non-transitory computer-readable media of claim 12, wherein storing the new training example in the centralized example database comprises storing the new training example in the centralized example database according to one or more options parameters that have been previously defined for the first application via the collection application programming interface, wherein the one or more options parameters comprise at least a time-to-live parameter that defines a period of time for which training examples are stored.
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August 11, 2017
August 2, 2022
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